Main network model diagnostics - unbalanced statistics
This file shows diagnostics for main network models fit using unbalanced racial/ethnic mixing matrices and degree terms as reported by egos. Here we try to identify which term added to a model with nodefactor(race) and nodematch(race) creates issues with MCMC diagnostics for race matching. This is motivated by observing, in the “dx_main_unbalanced_buildup.Rmd” file that the MCMC diagnostics for race matching are off when added to a model with nodefactor(race), nodefactor(region), and nodefactor(deg.pers). We will see if we can get it to fit in the absence of these other nodefactor terms and then see how the fit changes with the addition of each alone.
Load packages and model fits
rm(list = ls())
suppressMessages(library("EpiModelHIV"))
library("latticeExtra")## Loading required package: lattice## Loading required package: RColorBrewerlibrary("knitr")
library("kableExtra")
load(file = "/homes/dpwhite/R/GitHub Repos/WHAMP/Model fits and simulations/Fit tests and debugging/est/fit.m.testracemix.unbal.rda")Model terms and control settings
| Terms | Model 1 | Model 2 | Model 3 | Model 4 |
|---|---|---|---|---|
| edges | 2240.5 | 2240.5 | 2240.5 | 2240.5 |
| nodefactor.deg.pers.1 | NA | 474.0 | NA | 474.0 |
| nodefactor.deg.pers.2 | NA | 605.0 | NA | 605.0 |
| nodefactor.race..wa.B | 208.0 | 208.0 | 208.0 | 208.0 |
| nodefactor.race..wa.H | 535.0 | 535.0 | 535.0 | 535.0 |
| nodefactor.region.EW | NA | NA | 445.6 | 445.6 |
| nodefactor.region.OW | NA | NA | 1278.1 | 1278.1 |
| nodematch.race..wa.B | 31.2 | 31.2 | 31.2 | 31.2 |
| nodematch.race..wa.H | 123.3 | 123.3 | 123.3 | 123.3 |
| nodematch.race..wa.O | 1638.9 | 1638.9 | 1638.9 | 1638.9 |
| degrange | 0.0 | 0.0 | 0.0 | 0.0 |
| nodematch.role.class.I | -Inf | -Inf | -Inf | -Inf |
| nodematch.role.class.R | -Inf | -Inf | -Inf | -Inf |
The control settings for these models are:
set.control.ergm = control.ergm(MCMC.interval = 1e+5,
MCMC.samplesize = 7500,
MCMC.burnin = 1e+6,
MPLE.max.dyad.types = 1e+7,
init.method = "zeros",
MCMLE.maxit = 400,
parallel = np/2,
parallel.type="PSOCK"))MCMC diagnostics
Model 1
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges -0.5888 29.090 0.16795 0.16760
## nodefactor.race..wa.B 2.2717 12.615 0.07283 0.07374
## nodefactor.race..wa.H 3.6319 17.782 0.10267 0.10820
## nodematch.race..wa.B -2.4341 4.881 0.02818 0.02958
## nodematch.race..wa.H -2.0348 8.758 0.05057 0.05943
## nodematch.race..wa.O 2.1653 26.548 0.15328 0.15360
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -57.50 -20.500 -0.500 18.5000 56.500
## nodefactor.race..wa.B -22.00 -5.997 2.003 11.0032 27.003
## nodefactor.race..wa.H -30.98 -7.978 4.022 16.0220 39.022
## nodematch.race..wa.B -11.18 -6.179 -2.179 0.8213 7.821
## nodematch.race..wa.H -19.31 -8.312 -2.312 3.6876 15.688
## nodematch.race..wa.O -49.89 -15.890 2.110 20.1103 54.110
##
##
## Sample statistics cross-correlations:
## edges nodefactor.race..wa.B
## edges 1.00000000 0.2620776764
## nodefactor.race..wa.B 0.26207768 1.0000000000
## nodefactor.race..wa.H 0.34743587 -0.0070735216
## nodematch.race..wa.B 0.09536128 0.5579924897
## nodematch.race..wa.H 0.15568397 0.0004510425
## nodematch.race..wa.O 0.80737391 -0.0805027114
## nodefactor.race..wa.H nodematch.race..wa.B
## edges 0.347435872 0.095361283
## nodefactor.race..wa.B -0.007073522 0.557992490
## nodefactor.race..wa.H 1.000000000 -0.004504263
## nodematch.race..wa.B -0.004504263 1.000000000
## nodematch.race..wa.H 0.642445047 -0.006654524
## nodematch.race..wa.O -0.074636377 0.024051407
## nodematch.race..wa.H nodematch.race..wa.O
## edges 0.1556839733 0.80737391
## nodefactor.race..wa.B 0.0004510425 -0.08050271
## nodefactor.race..wa.H 0.6424450469 -0.07463638
## nodematch.race..wa.B -0.0066545236 0.02405141
## nodematch.race..wa.H 1.0000000000 0.06873998
## nodematch.race..wa.O 0.0687399776 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.004395513 0.020419275 0.060859744
## Lag 2e+05 -0.026191457 0.008962724 0.011118240
## Lag 3e+05 -0.013976462 0.014586038 0.005848958
## Lag 4e+05 0.023216152 -0.025990394 -0.004107018
## Lag 5e+05 0.031064558 -0.010150850 0.019695546
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.066105480 0.161483610 0.010185769
## Lag 2e+05 0.021644962 0.033158792 -0.008212128
## Lag 3e+05 0.001045170 -0.008162836 -0.002631588
## Lag 4e+05 -0.004031994 -0.003182427 0.001126897
## Lag 5e+05 -0.011051796 0.026007860 -0.012187514
## Chain 2
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.003397064 0.054976439 0.026977348
## Lag 2e+05 -0.019824387 0.015191315 0.003613058
## Lag 3e+05 0.013613367 -0.005098036 0.013292039
## Lag 4e+05 -0.015340242 0.001589431 -0.013265100
## Lag 5e+05 0.006313155 0.017459128 -0.009721128
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.030387541 0.151467123 -0.006905525
## Lag 2e+05 -0.007348548 0.013167747 -0.011246920
## Lag 3e+05 -0.006935945 -0.005524852 0.006485847
## Lag 4e+05 -0.012635452 -0.014149893 -0.008860804
## Lag 5e+05 0.003289638 -0.028582867 0.005429486
## Chain 3
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0166249912 0.013802121 0.059533648
## Lag 2e+05 -0.0168914583 0.003975351 0.007103057
## Lag 3e+05 -0.0083286402 -0.031094416 0.002127982
## Lag 4e+05 0.0233799893 -0.020900812 -0.009523470
## Lag 5e+05 -0.0008329317 0.014320530 -0.015748336
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.054563286 0.172602838 0.001849392
## Lag 2e+05 0.021204629 0.027586768 0.003891533
## Lag 3e+05 -0.038873026 -0.006653445 -0.008710939
## Lag 4e+05 -0.020848296 -0.007238560 0.014145819
## Lag 5e+05 -0.007284489 -0.016159381 -0.004217299
## Chain 4
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.002252247 -0.008296957 0.075434941
## Lag 2e+05 0.012914053 -0.006995296 -0.017109464
## Lag 3e+05 0.023383618 0.008620317 0.006418072
## Lag 4e+05 -0.011699866 -0.035683585 0.021846200
## Lag 5e+05 0.043066601 0.007206307 -0.012936708
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.04084675 0.166542942 -0.009338227
## Lag 2e+05 0.01134967 -0.007220387 0.002630321
## Lag 3e+05 -0.02397941 -0.004189423 0.008611681
## Lag 4e+05 -0.02469803 0.024831086 -0.009281773
## Lag 5e+05 -0.01672396 -0.009808596 0.038836696
## Chain 5
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.003338815 -0.003062388 0.053921333
## Lag 2e+05 -0.002243389 0.017845937 0.015116882
## Lag 3e+05 0.010767833 -0.007683173 -0.002680704
## Lag 4e+05 0.000516932 -0.007580719 -0.021479767
## Lag 5e+05 -0.004003865 -0.015759195 0.007424889
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.048077229 0.156754289 -0.006880084
## Lag 2e+05 0.029449368 0.029172676 -0.010402550
## Lag 3e+05 0.019784050 0.002776108 -0.002020655
## Lag 4e+05 0.006373965 -0.004178073 0.011031636
## Lag 5e+05 -0.001101402 0.014639249 -0.006909848
## Chain 6
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.0000000000 1.00000000
## Lag 1e+05 0.028297311 0.0415389514 0.04650799
## Lag 2e+05 -0.006908908 -0.0089962284 0.01393627
## Lag 3e+05 -0.038042938 0.0008218908 0.01464797
## Lag 4e+05 0.014542226 -0.0201398443 -0.01606172
## Lag 5e+05 0.003590850 -0.0113321288 -0.01108692
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.041175504 0.1733519157 0.014900510
## Lag 2e+05 0.032830697 -0.0108903813 -0.015348452
## Lag 3e+05 -0.002298667 -0.0208981511 -0.003092752
## Lag 4e+05 -0.004047677 -0.0102521208 0.037025537
## Lag 5e+05 -0.025709261 0.0000272084 0.004692202
## Chain 7
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000e+00 1.000000000 1.000000000
## Lag 1e+05 1.744591e-02 0.004978197 0.060463659
## Lag 2e+05 8.299807e-03 -0.011936628 -0.015451127
## Lag 3e+05 -3.255698e-02 -0.019543052 -0.004381679
## Lag 4e+05 -4.193396e-05 0.005997974 -0.020359970
## Lag 5e+05 1.864253e-02 -0.022160024 -0.002338753
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.00000000 1.0000000000
## Lag 1e+05 0.071962489 0.18865793 0.0189374020
## Lag 2e+05 -0.005048535 0.02244226 0.0286729141
## Lag 3e+05 0.004111172 -0.02101450 -0.0211628960
## Lag 4e+05 -0.008814520 -0.02880465 0.0003342111
## Lag 5e+05 -0.010103967 0.01393777 0.0120490514
## Chain 8
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.0000000000 1.000000e+00
## Lag 1e+05 -0.002662988 -0.0007490523 3.236644e-02
## Lag 2e+05 0.004546091 0.0133263894 1.462424e-02
## Lag 3e+05 -0.002668186 -0.0051560954 2.777050e-03
## Lag 4e+05 0.015900162 -0.0303057837 -8.123473e-05
## Lag 5e+05 -0.015075875 0.0010262825 -1.480856e-02
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.05566385 0.181118613 -0.032220069
## Lag 2e+05 -0.03116564 0.043493230 0.002258639
## Lag 3e+05 -0.03484217 0.032881937 0.000263964
## Lag 4e+05 -0.01074866 0.001968077 0.006899068
## Lag 5e+05 0.01936004 -0.005254293 -0.023132816
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.3567 1.0125 -0.8170
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.8573 -0.6792 0.4904
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.7213272 0.3113035 0.4139214
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.3912822 0.4969950 0.6238513
## Joint P-value (lower = worse): 0.9364104 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.4239 0.0314 -1.1925
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.1454 -1.7723 0.6594
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.67163659 0.97494739 0.23306955
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.88440555 0.07633805 0.50962689
## Joint P-value (lower = worse): 0.7362921 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.3578 0.7206 1.0449
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -0.7166 0.0958 -1.4475
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.7204643 0.4711506 0.2960875
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.4735965 0.9236777 0.1477468
## Joint P-value (lower = worse): 0.482485 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.8455 0.3176 -1.4854
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -1.2135 -1.2084 -0.7772
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.3978325 0.7507771 0.1374440
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.2249442 0.2269111 0.4370459
## Joint P-value (lower = worse): 0.364932 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.2661 0.1866 -0.2966
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 1.1905 1.0921 0.4786
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.7901500 0.8519839 0.7667849
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.2338670 0.2748012 0.6322126
## Joint P-value (lower = worse): 0.4862998 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 1.8432 -1.4042 1.3774
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -0.8573 -0.1859 1.7786
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.06529489 0.16027117 0.16837747
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.39127586 0.85253463 0.07530033
## Joint P-value (lower = worse): 0.1361442 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -2.1351 -0.3087 0.1256
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.1273 0.9859 -1.8784
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.03275344 0.75756562 0.90004783
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.89870580 0.32418776 0.06033228
## Joint P-value (lower = worse): 0.3907398 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.9140 -0.8390 -0.7464
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## -0.1839 -2.3059 0.9874
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.36073577 0.40149645 0.45543046
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## 0.85407010 0.02111746 0.32343032
## Joint P-value (lower = worse): 0.1765716 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 2
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges -2.24497 28.947 0.16713 0.16586
## nodefactor.deg.pers.1 0.28153 17.536 0.10125 0.10213
## nodefactor.deg.pers.2 0.05153 18.848 0.10882 0.10980
## nodefactor.race..wa.B 2.12433 12.477 0.07203 0.07517
## nodefactor.race..wa.H 2.22470 17.784 0.10267 0.10759
## nodematch.race..wa.B -2.20862 4.911 0.02835 0.03070
## nodematch.race..wa.H -2.11150 8.740 0.05046 0.06100
## nodematch.race..wa.O 2.21252 26.284 0.15175 0.15046
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -59.50 -21.500 -2.500 16.7500 54.500
## nodefactor.deg.pers.1 -34.00 -12.000 0.000 12.0000 35.000
## nodefactor.deg.pers.2 -37.00 -13.000 0.000 13.0000 37.000
## nodefactor.race..wa.B -22.00 -5.997 2.003 11.0032 27.003
## nodefactor.race..wa.H -32.98 -9.978 2.022 14.0220 37.022
## nodematch.race..wa.B -11.18 -5.179 -2.179 0.8213 7.821
## nodematch.race..wa.H -19.31 -8.312 -2.312 3.6876 14.688
## nodematch.race..wa.O -48.89 -15.890 2.110 20.1103 54.110
##
##
## Sample statistics cross-correlations:
## edges nodefactor.deg.pers.1
## edges 1.0000000 0.39335299
## nodefactor.deg.pers.1 0.3933530 1.00000000
## nodefactor.deg.pers.2 0.4270905 0.03918501
## nodefactor.race..wa.B 0.2631131 0.09444063
## nodefactor.race..wa.H 0.3561295 0.13959362
## nodematch.race..wa.B 0.1011940 0.02802289
## nodematch.race..wa.H 0.1555471 0.06478973
## nodematch.race..wa.O 0.8061054 0.32071201
## nodefactor.deg.pers.2 nodefactor.race..wa.B
## edges 0.42709051 0.263113107
## nodefactor.deg.pers.1 0.03918501 0.094440628
## nodefactor.deg.pers.2 1.00000000 0.112233076
## nodefactor.race..wa.B 0.11223308 1.000000000
## nodefactor.race..wa.H 0.13056521 -0.001705605
## nodematch.race..wa.B 0.04108212 0.565636528
## nodematch.race..wa.H 0.05650643 0.005843361
## nodematch.race..wa.O 0.35521654 -0.076127341
## nodefactor.race..wa.H nodematch.race..wa.B
## edges 0.356129519 0.101193953
## nodefactor.deg.pers.1 0.139593621 0.028022889
## nodefactor.deg.pers.2 0.130565211 0.041082125
## nodefactor.race..wa.B -0.001705605 0.565636528
## nodefactor.race..wa.H 1.000000000 -0.001890924
## nodematch.race..wa.B -0.001890924 1.000000000
## nodematch.race..wa.H 0.640739849 -0.001066902
## nodematch.race..wa.O -0.070866155 0.030719789
## nodematch.race..wa.H nodematch.race..wa.O
## edges 0.155547084 0.80610535
## nodefactor.deg.pers.1 0.064789727 0.32071201
## nodefactor.deg.pers.2 0.056506433 0.35521654
## nodefactor.race..wa.B 0.005843361 -0.07612734
## nodefactor.race..wa.H 0.640739849 -0.07086615
## nodematch.race..wa.B -0.001066902 0.03071979
## nodematch.race..wa.H 1.000000000 0.06733397
## nodematch.race..wa.O 0.067333973 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.018277377 0.028991110 0.003190081
## Lag 2e+05 0.026438055 -0.023529167 0.021572302
## Lag 3e+05 -0.029534118 0.017183717 0.010699079
## Lag 4e+05 0.017638859 0.002439095 0.015823106
## Lag 5e+05 -0.007400578 -0.006368242 -0.008162961
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.041785575 0.055658677 0.055476348
## Lag 2e+05 -0.001498313 0.011302634 -0.008753461
## Lag 3e+05 0.038762776 0.003132148 0.007604190
## Lag 4e+05 -0.007342283 0.011759988 0.019184268
## Lag 5e+05 -0.010082251 0.003783970 -0.017713469
## nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.199254562 0.008101128
## Lag 2e+05 0.024811737 0.017487419
## Lag 3e+05 0.017063647 -0.016174686
## Lag 4e+05 -0.003389890 0.011855810
## Lag 5e+05 -0.009120107 -0.003660872
## Chain 2
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.029567661 0.013355870 -0.0006411415
## Lag 2e+05 -0.021766894 0.004103819 0.0056186757
## Lag 3e+05 -0.008913515 0.009776907 0.0259955518
## Lag 4e+05 -0.016390331 -0.001591314 -0.0225515870
## Lag 5e+05 -0.005923561 -0.040061343 -0.0068173171
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.047483101 0.040277538 0.068175602
## Lag 2e+05 0.008866680 0.008316648 0.005941755
## Lag 3e+05 -0.033882174 -0.015103867 -0.016506273
## Lag 4e+05 -0.007186705 0.004160272 0.006328271
## Lag 5e+05 -0.003018393 0.011951116 -0.012584829
## nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.186285461 0.019461861
## Lag 2e+05 0.013631188 -0.023550918
## Lag 3e+05 -0.004226715 0.002461332
## Lag 4e+05 0.003400592 -0.003269980
## Lag 5e+05 0.042148891 0.015226264
## Chain 3
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.003715166 0.018392817 0.0006485533
## Lag 2e+05 0.012321491 0.032374189 -0.0129937509
## Lag 3e+05 -0.028330959 0.003448576 -0.0205698494
## Lag 4e+05 0.034412835 -0.016338562 0.0256153599
## Lag 5e+05 -0.003958013 -0.005696274 0.0111844075
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.020134688 0.046582249 0.052756296
## Lag 2e+05 -0.022929492 -0.024422890 -0.014319321
## Lag 3e+05 -0.006335654 -0.002215978 0.010974705
## Lag 4e+05 0.001715782 0.004162756 0.002214300
## Lag 5e+05 0.017009454 -0.040035143 0.003783662
## nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.0000000000
## Lag 1e+05 0.188209354 -0.0106325228
## Lag 2e+05 0.024188101 0.0221375341
## Lag 3e+05 -0.018310695 0.0035866727
## Lag 4e+05 -0.009310719 0.0119614526
## Lag 5e+05 -0.035185469 0.0002307658
## Chain 4
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.003348819 -0.0008352219 0.032362076
## Lag 2e+05 0.017095008 0.0212950751 0.036017567
## Lag 3e+05 0.013626256 0.0248384720 0.012841842
## Lag 4e+05 0.016840755 -0.0241983085 -0.007146828
## Lag 5e+05 -0.002184000 0.0089986921 -0.005684060
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.009323487 0.06374721 0.059855354
## Lag 2e+05 -0.003083647 0.02971856 -0.005707967
## Lag 3e+05 0.014362124 -0.01236926 0.035308340
## Lag 4e+05 0.036683420 -0.00413593 0.010107098
## Lag 5e+05 -0.003635447 -0.01548344 -0.010643044
## nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.195574599 -0.013685253
## Lag 2e+05 0.042548884 0.019816074
## Lag 3e+05 -0.017728709 0.022349134
## Lag 4e+05 -0.008521228 0.009983604
## Lag 5e+05 0.002949536 0.001124482
## Chain 5
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.010446072 -0.002826157 0.005408614
## Lag 2e+05 -0.002538888 -0.015960206 -0.020941692
## Lag 3e+05 -0.013833980 0.014149105 -0.015593998
## Lag 4e+05 0.011383457 0.009084746 0.008373701
## Lag 5e+05 -0.008609337 0.011510482 -0.021880759
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.022632180 0.044881831 0.069635201
## Lag 2e+05 0.005181617 0.026615354 0.014556965
## Lag 3e+05 0.007897557 -0.009521328 -0.009280946
## Lag 4e+05 -0.012718004 -0.031144696 -0.001446917
## Lag 5e+05 0.004333361 -0.003576541 0.016237099
## nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.0000000000 1.000000000
## Lag 1e+05 0.1546621958 0.024912367
## Lag 2e+05 0.0106362055 -0.001605056
## Lag 3e+05 -0.0031451364 -0.004797163
## Lag 4e+05 -0.0111190964 0.043440311
## Lag 5e+05 -0.0006078774 -0.031972813
## Chain 6
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.015353993 0.0226592567 -0.015739718
## Lag 2e+05 -0.007378274 -0.0052808074 0.003226649
## Lag 3e+05 -0.017927112 0.0029933093 0.026943214
## Lag 4e+05 0.022627308 -0.0007170325 0.016426287
## Lag 5e+05 -0.004492245 -0.0068562198 -0.003191460
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.028997256 0.030268526 0.050421637
## Lag 2e+05 -0.004272918 0.006619729 0.041386370
## Lag 3e+05 0.006386632 0.002255521 -0.009798439
## Lag 4e+05 0.015439194 0.007748616 0.022572924
## Lag 5e+05 0.006895521 0.020689514 0.001612679
## nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.00000000 1.000000000
## Lag 1e+05 0.16839302 -0.034505196
## Lag 2e+05 0.02469710 0.029313626
## Lag 3e+05 0.02235703 -0.002206819
## Lag 4e+05 0.02419970 0.016419407
## Lag 5e+05 0.03330881 -0.005427539
## Chain 7
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 -0.003499738 -0.00390604 0.007548034
## Lag 2e+05 -0.002911506 -0.01589353 0.015700302
## Lag 3e+05 -0.021500833 -0.00250599 -0.007505759
## Lag 4e+05 -0.009045602 -0.01012937 0.015867983
## Lag 5e+05 -0.011466993 -0.01515980 -0.010973133
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.038966988 0.07365700 0.058076122
## Lag 2e+05 0.025094290 0.02195529 0.031656156
## Lag 3e+05 0.004860069 -0.01595463 0.027273709
## Lag 4e+05 -0.009694329 -0.01712136 0.040882497
## Lag 5e+05 -0.001187022 -0.02065514 -0.003371861
## nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.00000000
## Lag 1e+05 0.201802979 -0.01463718
## Lag 2e+05 0.041246898 -0.01168662
## Lag 3e+05 0.022912624 -0.01607396
## Lag 4e+05 -0.001065059 -0.01097764
## Lag 5e+05 0.013131518 -0.02670544
## Chain 8
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.013995808 0.012185962 0.012836960
## Lag 2e+05 0.008795805 0.027235953 0.012480300
## Lag 3e+05 0.004118789 -0.026235372 -0.002879944
## Lag 4e+05 0.008225470 0.001338352 0.000547836
## Lag 5e+05 -0.008273032 0.011656126 0.030974039
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## Lag 0 1.0000000000 1.00000000 1.000000000
## Lag 1e+05 0.0003997273 0.05859716 0.058301372
## Lag 2e+05 -0.0053557547 0.01818210 0.012509443
## Lag 3e+05 0.0021546900 0.03160297 0.012361007
## Lag 4e+05 -0.0256387516 -0.02187574 0.005269084
## Lag 5e+05 -0.0391240384 0.01087467 -0.024784988
## nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.0000000000
## Lag 1e+05 0.186674420 -0.0317869055
## Lag 2e+05 0.026904656 0.0200257896
## Lag 3e+05 0.031769288 -0.0008415584
## Lag 4e+05 -0.005676078 0.0217211946
## Lag 5e+05 0.018190777 0.0078347328
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -1.4435 0.1741 -0.6186
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## -1.1810 0.2378 -1.0584
## nodematch.race..wa.H nodematch.race..wa.O
## 2.0112 -0.5305
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.14887771 0.86181381 0.53620663
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## 0.23760962 0.81202733 0.28987085
## nodematch.race..wa.H nodematch.race..wa.O
## 0.04430062 0.59578496
## Joint P-value (lower = worse): 0.2049696 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -1.20882 -0.04722 0.33818
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## -0.26059 1.66878 -0.01359
## nodematch.race..wa.H nodematch.race..wa.O
## 0.44651 -2.15089
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.22673379 0.96234185 0.73523083
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## 0.79440754 0.09516025 0.98915642
## nodematch.race..wa.H nodematch.race..wa.O
## 0.65522828 0.03148515
## Joint P-value (lower = worse): 0.3738561 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -0.08882 -0.06079 -0.20455
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## 1.01732 -0.63237 1.35506
## nodematch.race..wa.H nodematch.race..wa.O
## -1.90809 -0.69164
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.92922709 0.95152952 0.83792448
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## 0.30900306 0.52714665 0.17539912
## nodematch.race..wa.H nodematch.race..wa.O
## 0.05638009 0.48916498
## Joint P-value (lower = worse): 0.5207301 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -0.31368 -0.24611 -0.07002
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## 0.19899 -1.45694 -0.52990
## nodematch.race..wa.H nodematch.race..wa.O
## -1.14837 0.03448
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.7537658 0.8055963 0.9441770
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## 0.8422723 0.1451335 0.5961814
## nodematch.race..wa.H nodematch.race..wa.O
## 0.2508163 0.9724913
## Joint P-value (lower = worse): 0.9511695 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -1.0668 0.7505 -1.7944
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## -0.4900 -0.1185 -1.6966
## nodematch.race..wa.H nodematch.race..wa.O
## 0.3628 -1.0278
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.28606822 0.45296483 0.07274395
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## 0.62415615 0.90568704 0.08977053
## nodematch.race..wa.H nodematch.race..wa.O
## 0.71677464 0.30402749
## Joint P-value (lower = worse): 0.4762237 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 2.0724 1.4372 0.3469
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## 1.0671 2.0869 0.5103
## nodematch.race..wa.H nodematch.race..wa.O
## 0.8852 0.8228
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.03822798 0.15064833 0.72867922
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## 0.28590848 0.03689677 0.60982521
## nodematch.race..wa.H nodematch.race..wa.O
## 0.37603992 0.41061557
## Joint P-value (lower = worse): 0.4280388 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -0.2904 1.2499 -0.3615
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## -0.1161 -2.0285 -0.5203
## nodematch.race..wa.H nodematch.race..wa.O
## -2.1074 0.1015
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.77151205 0.21134666 0.71772647
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## 0.90756437 0.04250855 0.60287714
## nodematch.race..wa.H nodematch.race..wa.O
## 0.03508278 0.91916311
## Joint P-value (lower = worse): 0.46786 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -0.35741 -0.30297 2.99597
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## -0.72409 -0.89094 -0.46070
## nodematch.race..wa.H nodematch.race..wa.O
## -1.02361 -0.05032
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.720788622 0.761913303 0.002735716
## nodefactor.race..wa.B nodefactor.race..wa.H nodematch.race..wa.B
## 0.469013441 0.372963212 0.645010769
## nodematch.race..wa.H nodematch.race..wa.O
## 0.306020539 0.959870372
## Joint P-value (lower = worse): 0.1407411 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 3
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges -2.50403 28.869 0.16668 0.16623
## nodefactor.race..wa.B 2.09613 12.574 0.07260 0.07358
## nodefactor.race..wa.H 1.91563 17.645 0.10187 0.10678
## nodefactor.region.EW -0.08727 16.058 0.09271 0.09408
## nodefactor.region.OW -0.83170 29.503 0.17034 0.17257
## nodematch.race..wa.B -2.24972 4.874 0.02814 0.02979
## nodematch.race..wa.H -2.22103 8.696 0.05021 0.05997
## nodematch.race..wa.O 2.14008 26.326 0.15199 0.15123
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -59.50 -22.500 -2.5000 16.5000 53.500
## nodefactor.race..wa.B -22.00 -5.997 2.0032 11.0032 27.003
## nodefactor.race..wa.H -32.98 -9.978 2.0220 14.0220 37.022
## nodefactor.region.EW -31.56 -10.561 0.4392 10.4392 31.439
## nodefactor.region.OW -59.13 -21.131 -1.1306 18.8694 56.869
## nodematch.race..wa.B -11.18 -5.179 -2.1787 0.8213 7.821
## nodematch.race..wa.H -19.31 -8.312 -2.3124 3.6876 14.688
## nodematch.race..wa.O -48.89 -15.890 2.1103 20.1103 54.110
##
##
## Sample statistics cross-correlations:
## edges nodefactor.race..wa.B
## edges 1.0000000 0.263224878
## nodefactor.race..wa.B 0.2632249 1.000000000
## nodefactor.race..wa.H 0.3457791 -0.008445144
## nodefactor.region.EW 0.3557890 0.042379687
## nodefactor.region.OW 0.6273515 0.130262577
## nodematch.race..wa.B 0.1038173 0.567372418
## nodematch.race..wa.H 0.1496551 0.002760818
## nodematch.race..wa.O 0.8077871 -0.077362438
## nodefactor.race..wa.H nodefactor.region.EW
## edges 0.345779055 0.355788990
## nodefactor.race..wa.B -0.008445144 0.042379687
## nodefactor.race..wa.H 1.000000000 0.241355829
## nodefactor.region.EW 0.241355829 1.000000000
## nodefactor.region.OW 0.195676435 0.055284443
## nodematch.race..wa.B 0.010434424 0.003540484
## nodematch.race..wa.H 0.636343509 0.127524509
## nodematch.race..wa.O -0.074898081 0.250932748
## nodefactor.region.OW nodematch.race..wa.B
## edges 0.62735152 0.103817350
## nodefactor.race..wa.B 0.13026258 0.567372418
## nodefactor.race..wa.H 0.19567644 0.010434424
## nodefactor.region.EW 0.05528444 0.003540484
## nodefactor.region.OW 1.00000000 0.045493419
## nodematch.race..wa.B 0.04549342 1.000000000
## nodematch.race..wa.H 0.08128490 0.009196197
## nodematch.race..wa.O 0.52986632 0.024046730
## nodematch.race..wa.H nodematch.race..wa.O
## edges 0.149655144 0.80778713
## nodefactor.race..wa.B 0.002760818 -0.07736244
## nodefactor.race..wa.H 0.636343509 -0.07489808
## nodefactor.region.EW 0.127524509 0.25093275
## nodefactor.region.OW 0.081284899 0.52986632
## nodematch.race..wa.B 0.009196197 0.02404673
## nodematch.race..wa.H 1.000000000 0.06830887
## nodematch.race..wa.O 0.068308874 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.00000000
## Lag 1e+05 -0.0051482255 0.018080087 0.07360187
## Lag 2e+05 -0.0287161820 -0.034061093 -0.01929460
## Lag 3e+05 -0.0118868525 0.008869350 -0.01216225
## Lag 4e+05 -0.0128259935 -0.010082551 0.02434032
## Lag 5e+05 0.0009062889 0.006874745 -0.01017732
## nodefactor.region.EW nodefactor.region.OW nodematch.race..wa.B
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.009676068 -0.003175540 0.0732514350
## Lag 2e+05 -0.041900088 -0.019532722 -0.0078588894
## Lag 3e+05 -0.005552732 0.011745033 -0.0004368906
## Lag 4e+05 -0.017504225 0.024883649 0.0018116174
## Lag 5e+05 -0.003616864 -0.004506722 0.0043842759
## nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.187966498 0.004321310
## Lag 2e+05 0.005610677 -0.028014534
## Lag 3e+05 -0.012156459 0.008978771
## Lag 4e+05 0.008994011 -0.023535051
## Lag 5e+05 -0.001515203 0.003668919
## Chain 2
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.012563194 -0.007854642 0.039111746
## Lag 2e+05 -0.037559057 0.010217969 0.022476928
## Lag 3e+05 -0.006951224 -0.008019785 -0.003467081
## Lag 4e+05 0.006083711 0.001243205 -0.006313127
## Lag 5e+05 -0.023525396 0.016212344 0.018326946
## nodefactor.region.EW nodefactor.region.OW nodematch.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.016557748 0.034831493 0.074526965
## Lag 2e+05 -0.027818755 -0.007811295 -0.006941076
## Lag 3e+05 0.012648468 0.010124211 -0.011482909
## Lag 4e+05 0.002195528 -0.005511849 -0.014409185
## Lag 5e+05 -0.002628903 0.011789198 0.027954073
## nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.0000000000 1.000000000
## Lag 1e+05 0.1806438377 0.018178618
## Lag 2e+05 0.0357517653 -0.028246994
## Lag 3e+05 -0.0029877950 0.005203405
## Lag 4e+05 0.0003973303 0.017037664
## Lag 5e+05 -0.0059652374 -0.016305115
## Chain 3
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0307702662 0.027658872 0.055937880
## Lag 2e+05 -0.0263087761 0.004076542 0.021847458
## Lag 3e+05 0.0002046685 -0.015185066 -0.008181475
## Lag 4e+05 0.0070076143 0.010151361 0.020801691
## Lag 5e+05 0.0040769354 -0.016319036 0.017820980
## nodefactor.region.EW nodefactor.region.OW nodematch.race..wa.B
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.048668625 0.01863045 0.039231623
## Lag 2e+05 -0.008612631 -0.02989991 0.013679940
## Lag 3e+05 -0.001862586 0.02275201 -0.008703889
## Lag 4e+05 0.014764573 0.00920284 0.010661858
## Lag 5e+05 0.009838090 -0.01007743 0.020744446
## nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.194433060 0.022242590
## Lag 2e+05 0.042855566 -0.036667996
## Lag 3e+05 0.007954734 -0.012179886
## Lag 4e+05 -0.011968980 0.009819413
## Lag 5e+05 -0.009866694 0.002893492
## Chain 4
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 0.002157829 0.01389553 0.061200101
## Lag 2e+05 0.007970824 0.03390481 -0.008291249
## Lag 3e+05 -0.004324041 -0.02153689 -0.008340931
## Lag 4e+05 -0.020455652 0.03867754 0.021025007
## Lag 5e+05 -0.008411448 -0.01705892 -0.007514773
## nodefactor.region.EW nodefactor.region.OW nodematch.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.021052922 0.031306261 0.062382057
## Lag 2e+05 0.001132258 -0.013720233 0.011496071
## Lag 3e+05 -0.008612242 -0.006297541 0.024500613
## Lag 4e+05 -0.024377525 0.007895268 -0.016736448
## Lag 5e+05 0.002172048 -0.020585531 -0.009950879
## nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.164977181 0.021037626
## Lag 2e+05 0.045736136 -0.018603930
## Lag 3e+05 0.003765938 0.020998196
## Lag 4e+05 0.014788824 -0.008541293
## Lag 5e+05 0.023880579 0.012420447
## Chain 5
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.001126976 -0.008614565 0.0632114065
## Lag 2e+05 0.004613867 0.001418082 0.0141330420
## Lag 3e+05 0.003549017 -0.009972491 0.0110560963
## Lag 4e+05 0.007962901 0.008042538 -0.0004663387
## Lag 5e+05 -0.016566930 0.040009064 0.0311918301
## nodefactor.region.EW nodefactor.region.OW nodematch.race..wa.B
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.012078496 -0.009092982 0.05481364
## Lag 2e+05 0.033204153 -0.024252546 0.01832972
## Lag 3e+05 0.009638882 0.008936184 -0.01628705
## Lag 4e+05 -0.010695274 0.013137050 0.01222808
## Lag 5e+05 -0.002749617 0.006983583 0.01975620
## nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000e+00
## Lag 1e+05 0.169516126 1.206951e-03
## Lag 2e+05 0.037889850 6.560842e-05
## Lag 3e+05 0.017798825 3.270498e-03
## Lag 4e+05 0.008062528 -9.132545e-03
## Lag 5e+05 -0.027796533 -5.184394e-03
## Chain 6
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000e+00 1.000000000 1.0000000000
## Lag 1e+05 -3.801329e-03 -0.022654120 0.0808603495
## Lag 2e+05 7.905742e-05 -0.006993734 0.0090119787
## Lag 3e+05 3.097944e-02 0.002779985 0.0284070163
## Lag 4e+05 -5.799793e-03 -0.031871457 0.0058081313
## Lag 5e+05 1.052638e-02 -0.003034963 -0.0000899058
## nodefactor.region.EW nodefactor.region.OW nodematch.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.025594789 0.013024831 0.060759362
## Lag 2e+05 0.007960598 -0.041899243 -0.020078295
## Lag 3e+05 0.016925718 0.024069972 0.009147984
## Lag 4e+05 0.010973474 -0.001723083 -0.029415178
## Lag 5e+05 0.003014535 0.010887558 -0.013560911
## nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.211935284 0.027545834
## Lag 2e+05 0.065184116 -0.002435683
## Lag 3e+05 0.013747252 0.006714949
## Lag 4e+05 0.016426588 -0.007313488
## Lag 5e+05 0.005216342 0.007058131
## Chain 7
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.004866641 -0.001795375 0.07480390
## Lag 2e+05 0.005293501 0.003272682 0.01787659
## Lag 3e+05 -0.002910028 0.023337536 0.01877912
## Lag 4e+05 -0.003667916 0.002467533 -0.02200063
## Lag 5e+05 0.001644688 0.017427330 0.03062236
## nodefactor.region.EW nodefactor.region.OW nodematch.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.004875493 0.002722852 0.073785970
## Lag 2e+05 -0.015566624 -0.012473483 -0.002374406
## Lag 3e+05 -0.008214824 -0.022981870 0.028128997
## Lag 4e+05 0.033901819 0.004410687 0.005995716
## Lag 5e+05 0.030203734 -0.027217539 0.009394454
## nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.0000000000 1.000000000
## Lag 1e+05 0.1775166002 -0.021924045
## Lag 2e+05 0.0436429948 -0.024193268
## Lag 3e+05 -0.0008622112 -0.006742400
## Lag 4e+05 -0.0050546218 0.002676825
## Lag 5e+05 0.0160058537 0.009065279
## Chain 8
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.001298655 0.027479917 0.041296691
## Lag 2e+05 0.022514068 -0.006181535 0.027315907
## Lag 3e+05 0.007479708 0.006476298 -0.006085228
## Lag 4e+05 -0.008678003 0.010079270 -0.007166217
## Lag 5e+05 0.004845089 0.018760252 0.015075027
## nodefactor.region.EW nodefactor.region.OW nodematch.race..wa.B
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.023813065 0.025216608 0.081269415
## Lag 2e+05 -0.006269052 -0.003469458 -0.021967368
## Lag 3e+05 -0.007364342 0.011082267 -0.002205280
## Lag 4e+05 -0.002836984 -0.025522067 0.009684236
## Lag 5e+05 0.015690835 0.005639452 -0.014880101
## nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.173644869 -0.008618537
## Lag 2e+05 0.047828855 0.007815577
## Lag 3e+05 -0.021092082 -0.002756049
## Lag 4e+05 -0.007505618 -0.007363422
## Lag 5e+05 -0.009964435 0.005413762
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -1.3043 -0.6521 -0.4722
## nodefactor.region.EW nodefactor.region.OW nodematch.race..wa.B
## 1.5510 -0.5527 -2.2323
## nodematch.race..wa.H nodematch.race..wa.O
## -0.7757 -1.3747
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.19212575 0.51434119 0.63679541
## nodefactor.region.EW nodefactor.region.OW nodematch.race..wa.B
## 0.12089084 0.58043616 0.02559293
## nodematch.race..wa.H nodematch.race..wa.O
## 0.43793572 0.16921070
## Joint P-value (lower = worse): 0.1073029 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 1.0434 1.0702 -0.8438
## nodefactor.region.EW nodefactor.region.OW nodematch.race..wa.B
## -0.1562 1.0659 0.6959
## nodematch.race..wa.H nodematch.race..wa.O
## -1.0831 0.9099
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.2967817 0.2845256 0.3987586
## nodefactor.region.EW nodefactor.region.OW nodematch.race..wa.B
## 0.8758374 0.2864753 0.4865113
## nodematch.race..wa.H nodematch.race..wa.O
## 0.2787442 0.3628855
## Joint P-value (lower = worse): 0.8380827 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.48163 0.49096 -2.05319
## nodefactor.region.EW nodefactor.region.OW nodematch.race..wa.B
## -0.91006 -0.09070 -0.07256
## nodematch.race..wa.H nodematch.race..wa.O
## -2.39470 -0.37769
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.63006878 0.62345305 0.04005425
## nodefactor.region.EW nodefactor.region.OW nodematch.race..wa.B
## 0.36278936 0.92772779 0.94215769
## nodematch.race..wa.H nodematch.race..wa.O
## 0.01663418 0.70566275
## Joint P-value (lower = worse): 0.5465915 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -1.7478 0.3612 0.5536
## nodefactor.region.EW nodefactor.region.OW nodematch.race..wa.B
## 0.3064 -1.6667 0.4119
## nodematch.race..wa.H nodematch.race..wa.O
## 1.0943 -1.8447
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.08050028 0.71795001 0.57986571
## nodefactor.region.EW nodefactor.region.OW nodematch.race..wa.B
## 0.75932869 0.09557519 0.68040209
## nodematch.race..wa.H nodematch.race..wa.O
## 0.27380954 0.06507637
## Joint P-value (lower = worse): 0.6469245 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.20784 -0.09335 -1.84936
## nodefactor.region.EW nodefactor.region.OW nodematch.race..wa.B
## 1.16278 -0.42246 0.32443
## nodematch.race..wa.H nodematch.race..wa.O
## -1.31605 0.64049
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.83535667 0.92562183 0.06440555
## nodefactor.region.EW nodefactor.region.OW nodematch.race..wa.B
## 0.24491676 0.67269256 0.74561361
## nodematch.race..wa.H nodematch.race..wa.O
## 0.18815772 0.52185227
## Joint P-value (lower = worse): 0.6296029 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.34967 -1.16833 -0.55616
## nodefactor.region.EW nodefactor.region.OW nodematch.race..wa.B
## 0.40577 0.44482 -2.05341
## nodematch.race..wa.H nodematch.race..wa.O
## 0.05933 1.00939
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.72658621 0.24267491 0.57810244
## nodefactor.region.EW nodefactor.region.OW nodematch.race..wa.B
## 0.68491118 0.65644873 0.04003329
## nodematch.race..wa.H nodematch.race..wa.O
## 0.95268844 0.31279000
## Joint P-value (lower = worse): 0.7497782 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.47853 0.82984 -0.20074
## nodefactor.region.EW nodefactor.region.OW nodematch.race..wa.B
## -1.40826 0.31724 -0.69919
## nodematch.race..wa.H nodematch.race..wa.O
## -0.17383 0.05448
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.6322724 0.4066304 0.8409046
## nodefactor.region.EW nodefactor.region.OW nodematch.race..wa.B
## 0.1590540 0.7510633 0.4844345
## nodematch.race..wa.H nodematch.race..wa.O
## 0.8619956 0.9565547
## Joint P-value (lower = worse): 0.7663956 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -1.6493 -2.0128 -0.9680
## nodefactor.region.EW nodefactor.region.OW nodematch.race..wa.B
## -1.7042 -0.4002 -2.4934
## nodematch.race..wa.H nodematch.race..wa.O
## -1.0170 -0.9602
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.09909273 0.04414036 0.33303466
## nodefactor.region.EW nodefactor.region.OW nodematch.race..wa.B
## 0.08833763 0.68902389 0.01265355
## nodematch.race..wa.H nodematch.race..wa.O
## 0.30916898 0.33696412
## Joint P-value (lower = worse): 0.1858979 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 4
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges -0.7821 28.964 0.16722 0.16870
## nodefactor.deg.pers.1 0.1774 17.500 0.10103 0.10103
## nodefactor.deg.pers.2 0.4216 18.624 0.10753 0.10478
## nodefactor.race..wa.B 2.8833 12.613 0.07282 0.07417
## nodefactor.race..wa.H 2.0190 17.653 0.10192 0.10925
## nodefactor.region.EW 0.0701 15.915 0.09189 0.09328
## nodefactor.region.OW 0.5392 29.570 0.17072 0.17082
## nodematch.race..wa.B -2.0920 4.938 0.02851 0.02987
## nodematch.race..wa.H -2.2595 8.669 0.05005 0.06142
## nodematch.race..wa.O 3.0909 26.405 0.15245 0.15215
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -57.50 -20.500 -0.5000 18.5000 56.500
## nodefactor.deg.pers.1 -34.00 -12.000 0.0000 12.0000 35.000
## nodefactor.deg.pers.2 -36.00 -12.000 0.0000 13.0000 37.000
## nodefactor.race..wa.B -22.00 -5.997 3.0032 11.0032 28.003
## nodefactor.race..wa.H -32.00 -9.978 2.0220 14.0220 37.022
## nodefactor.region.EW -31.56 -10.561 0.4392 10.4392 31.439
## nodefactor.region.OW -57.13 -19.131 -0.1306 20.8694 58.869
## nodematch.race..wa.B -11.18 -5.179 -2.1787 0.8213 7.821
## nodematch.race..wa.H -19.31 -8.312 -2.3124 3.6876 14.688
## nodematch.race..wa.O -48.89 -14.890 3.1103 21.1103 55.110
##
##
## Sample statistics cross-correlations:
## edges nodefactor.deg.pers.1
## edges 1.0000000 0.39769960
## nodefactor.deg.pers.1 0.3976996 1.00000000
## nodefactor.deg.pers.2 0.4216276 0.04530148
## nodefactor.race..wa.B 0.2666542 0.10355680
## nodefactor.race..wa.H 0.3459975 0.13563464
## nodefactor.region.EW 0.3646620 0.14292194
## nodefactor.region.OW 0.6297898 0.24491207
## nodematch.race..wa.B 0.1042821 0.04438147
## nodematch.race..wa.H 0.1527024 0.06208016
## nodematch.race..wa.O 0.8078509 0.32477544
## nodefactor.deg.pers.2 nodefactor.race..wa.B
## edges 0.42162756 0.266654153
## nodefactor.deg.pers.1 0.04530148 0.103556805
## nodefactor.deg.pers.2 1.00000000 0.113910469
## nodefactor.race..wa.B 0.11391047 1.000000000
## nodefactor.race..wa.H 0.12407506 -0.005979985
## nodefactor.region.EW 0.14390008 0.042017498
## nodefactor.region.OW 0.27389656 0.134666468
## nodematch.race..wa.B 0.04238212 0.566763765
## nodematch.race..wa.H 0.05621395 0.004733988
## nodematch.race..wa.O 0.35150461 -0.073658062
## nodefactor.race..wa.H nodefactor.region.EW
## edges 3.459975e-01 0.36466201
## nodefactor.deg.pers.1 1.356346e-01 0.14292194
## nodefactor.deg.pers.2 1.240751e-01 0.14390008
## nodefactor.race..wa.B -5.979985e-03 0.04201750
## nodefactor.race..wa.H 1.000000e+00 0.25136878
## nodefactor.region.EW 2.513688e-01 1.00000000
## nodefactor.region.OW 1.996581e-01 0.06922304
## nodematch.race..wa.B 1.296239e-05 0.01342154
## nodematch.race..wa.H 6.369781e-01 0.13514032
## nodematch.race..wa.O -7.702702e-02 0.25875691
## nodefactor.region.OW nodematch.race..wa.B
## edges 0.62978978 1.042821e-01
## nodefactor.deg.pers.1 0.24491207 4.438147e-02
## nodefactor.deg.pers.2 0.27389656 4.238212e-02
## nodefactor.race..wa.B 0.13466647 5.667638e-01
## nodefactor.race..wa.H 0.19965809 1.296239e-05
## nodefactor.region.EW 0.06922304 1.342154e-02
## nodefactor.region.OW 1.00000000 4.570808e-02
## nodematch.race..wa.B 0.04570808 1.000000e+00
## nodematch.race..wa.H 0.08321858 -6.381321e-03
## nodematch.race..wa.O 0.52888189 2.854810e-02
## nodematch.race..wa.H nodematch.race..wa.O
## edges 0.152702367 0.80785094
## nodefactor.deg.pers.1 0.062080160 0.32477544
## nodefactor.deg.pers.2 0.056213954 0.35150461
## nodefactor.race..wa.B 0.004733988 -0.07365806
## nodefactor.race..wa.H 0.636978053 -0.07702702
## nodefactor.region.EW 0.135140320 0.25875691
## nodefactor.region.OW 0.083218583 0.52888189
## nodematch.race..wa.B -0.006381321 0.02854810
## nodematch.race..wa.H 1.000000000 0.06651993
## nodematch.race..wa.O 0.066519926 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.031613433 0.003436771 0.019441663
## Lag 2e+05 -0.013746415 0.005345912 0.007133517
## Lag 3e+05 -0.016546318 -0.031646036 0.021901679
## Lag 4e+05 -0.031639351 -0.027769658 -0.018652234
## Lag 5e+05 0.002456981 -0.007232210 0.014330005
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.025104982 0.047565004 0.058573711
## Lag 2e+05 -0.007881707 -0.030164270 -0.005288859
## Lag 3e+05 0.006751759 0.002594065 0.007794081
## Lag 4e+05 -0.014652234 -0.016348591 -0.017913325
## Lag 5e+05 0.018856239 0.002435978 0.015947083
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.006048924 0.0575223682 0.200163768
## Lag 2e+05 0.009212617 -0.0115202469 0.034807370
## Lag 3e+05 0.006088956 -0.0130525852 0.003149347
## Lag 4e+05 -0.008155319 0.0003809718 -0.030085302
## Lag 5e+05 0.012771694 0.0202498481 0.011940330
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 0.044145128
## Lag 2e+05 -0.015380097
## Lag 3e+05 -0.017490316
## Lag 4e+05 0.003478832
## Lag 5e+05 0.014105809
## Chain 2
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.015936644 0.0165044690 0.006559786
## Lag 2e+05 -0.013840681 -0.0002018044 0.003627080
## Lag 3e+05 0.001833094 0.0176939797 0.028108855
## Lag 4e+05 0.040373402 -0.0201200479 -0.030665631
## Lag 5e+05 0.022616940 0.0007472339 0.004966585
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 -0.007441850 0.03840781 0.016720258
## Lag 2e+05 0.008517708 0.01626892 0.008542367
## Lag 3e+05 0.011610738 -0.01890772 -0.005001262
## Lag 4e+05 0.006026599 -0.02893857 0.014945164
## Lag 5e+05 -0.019363627 -0.01727607 -0.012097725
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.006959333 0.011294831 0.217096868
## Lag 2e+05 0.024932658 0.009940937 0.040619004
## Lag 3e+05 -0.003985981 0.002896406 0.012630105
## Lag 4e+05 -0.002007182 0.038433159 0.006931709
## Lag 5e+05 -0.021728270 -0.012342561 -0.020886549
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 -0.008250764
## Lag 2e+05 -0.007101318
## Lag 3e+05 0.010549961
## Lag 4e+05 0.026465119
## Lag 5e+05 0.017362599
## Chain 3
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.019794438 -0.019959332 -0.008168591
## Lag 2e+05 0.009659463 0.002705125 0.029300856
## Lag 3e+05 -0.013490379 -0.008174919 -0.006936940
## Lag 4e+05 -0.002536747 0.004527562 0.011324225
## Lag 5e+05 0.002669543 0.018438933 -0.020150801
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.03019365 0.071521403 0.003058476
## Lag 2e+05 0.02692368 0.037707177 -0.006099224
## Lag 3e+05 -0.02104725 -0.006448694 0.009189922
## Lag 4e+05 0.03446706 -0.006818910 0.013687224
## Lag 5e+05 0.03832081 0.001204360 0.013723439
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.004910225 0.033751527 0.177548124
## Lag 2e+05 0.020579824 0.025672562 0.061952618
## Lag 3e+05 -0.025834391 -0.013622571 0.012235136
## Lag 4e+05 -0.020379349 -0.008224187 -0.013800947
## Lag 5e+05 -0.001368661 0.031796939 -0.009244936
## nodematch.race..wa.O
## Lag 0 1.0000000000
## Lag 1e+05 0.0150508851
## Lag 2e+05 -0.0009015168
## Lag 3e+05 -0.0078086102
## Lag 4e+05 -0.0225016055
## Lag 5e+05 0.0121260452
## Chain 4
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000e+00 1.000000000
## Lag 1e+05 0.024789572 9.774336e-03 0.017185684
## Lag 2e+05 -0.040033508 -1.498816e-02 -0.023275756
## Lag 3e+05 0.014025044 5.151470e-03 -0.009889346
## Lag 4e+05 -0.005559105 8.161177e-05 -0.026888561
## Lag 5e+05 0.015469787 1.076395e-02 0.002865431
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.034561910 0.058966627 0.014235081
## Lag 2e+05 -0.004313803 -0.021996908 -0.004842740
## Lag 3e+05 0.025209197 0.008157381 0.004643023
## Lag 4e+05 0.008771794 -0.015833769 0.001356796
## Lag 5e+05 0.016242889 -0.012489013 -0.010018388
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.0000000000 1.00000000
## Lag 1e+05 0.025913319 0.0605196465 0.18990476
## Lag 2e+05 -0.017508844 0.0006247318 0.04667426
## Lag 3e+05 0.031423322 0.0082089296 0.03419479
## Lag 4e+05 -0.033671693 -0.0038137819 0.01216983
## Lag 5e+05 -0.009439765 0.0146424327 -0.01372970
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 0.008435523
## Lag 2e+05 -0.040274830
## Lag 3e+05 0.016696965
## Lag 4e+05 -0.002804304
## Lag 5e+05 0.011650563
## Chain 5
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.002708544 -0.008652645 0.0066190268
## Lag 2e+05 0.004219943 -0.003884967 -0.0034821032
## Lag 3e+05 -0.004698070 0.011726916 -0.0458092797
## Lag 4e+05 -0.035979053 -0.027168968 -0.0300695084
## Lag 5e+05 -0.018133352 -0.008584440 -0.0007087911
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.0000000000
## Lag 1e+05 0.010334793 0.07716029 0.0003957808
## Lag 2e+05 -0.005537630 0.02833679 0.0258407181
## Lag 3e+05 0.014139884 0.03808707 -0.0021766016
## Lag 4e+05 0.029548608 0.02394257 0.0252239307
## Lag 5e+05 -0.009143647 0.03313241 -0.0103113404
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.00000000 1.000000000 1.00000000
## Lag 1e+05 0.01747162 0.056253122 0.21473499
## Lag 2e+05 0.01156516 0.026100891 0.03822764
## Lag 3e+05 -0.02227493 0.001314315 0.03532601
## Lag 4e+05 -0.02216020 0.008461128 0.02312065
## Lag 5e+05 -0.02120181 -0.015461878 0.01232091
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 0.009687080
## Lag 2e+05 0.002672384
## Lag 3e+05 -0.005010374
## Lag 4e+05 -0.017255729
## Lag 5e+05 -0.014243104
## Chain 6
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0257383767 -0.006114734 0.014571620
## Lag 2e+05 0.0066660721 0.001148262 0.024593184
## Lag 3e+05 0.0166011373 -0.003719335 0.007122277
## Lag 4e+05 -0.0004137175 -0.027355711 0.005559421
## Lag 5e+05 0.0010630218 -0.017767751 0.033156446
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.01766730 0.074436384 0.014787594
## Lag 2e+05 0.02293558 0.010256860 -0.008428963
## Lag 3e+05 -0.02595264 -0.006861768 -0.003652313
## Lag 4e+05 0.00191971 -0.017186115 -0.023776685
## Lag 5e+05 0.01164023 -0.006159605 -0.008598749
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.009775661 0.095815095 0.198710643
## Lag 2e+05 -0.016908693 0.006770842 0.018048374
## Lag 3e+05 0.005376860 -0.018422234 -0.002411409
## Lag 4e+05 0.014563799 -0.005197572 0.013440276
## Lag 5e+05 -0.009593660 -0.018037344 0.016161255
## nodematch.race..wa.O
## Lag 0 1.0000000000
## Lag 1e+05 0.0223747650
## Lag 2e+05 0.0051237668
## Lag 3e+05 0.0051499259
## Lag 4e+05 -0.0005848489
## Lag 5e+05 -0.0055415635
## Chain 7
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.009104818 0.0099204665 0.004679962
## Lag 2e+05 0.013931793 0.0310362789 -0.008536338
## Lag 3e+05 0.008861808 0.0125728754 -0.012860709
## Lag 4e+05 -0.016737732 0.0095737222 0.022197048
## Lag 5e+05 -0.019072434 0.0001486868 0.008362994
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.007843112 0.081248871 0.0113510935
## Lag 2e+05 -0.013461012 0.016983983 -0.0104543074
## Lag 3e+05 0.019285994 -0.002414052 -0.0004951615
## Lag 4e+05 -0.017235239 -0.039919032 0.0135100916
## Lag 5e+05 0.037141666 0.006751605 0.0053512632
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.017315287 0.068006618 0.203438702
## Lag 2e+05 0.020027743 -0.010949073 0.046977873
## Lag 3e+05 -0.013853200 -0.017101612 0.036080456
## Lag 4e+05 -0.010777524 -0.006183436 0.003953253
## Lag 5e+05 0.006784148 0.010211798 0.007295238
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 0.001827574
## Lag 2e+05 -0.002955092
## Lag 3e+05 0.003997275
## Lag 4e+05 -0.001977305
## Lag 5e+05 -0.011833027
## Chain 8
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## Lag 0 1.0000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.0021879140 0.0024272944 -0.020557623
## Lag 2e+05 -0.0212683883 0.0023466103 -0.035154257
## Lag 3e+05 0.0058293953 0.0104313533 0.007137339
## Lag 4e+05 0.0141486817 -0.0009908951 -0.002479882
## Lag 5e+05 -0.0005044316 0.0074317821 -0.008584502
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.009223159 0.041259420 0.013661865
## Lag 2e+05 -0.018475608 0.009882163 -0.013147975
## Lag 3e+05 0.008384685 0.006643589 0.055859686
## Lag 4e+05 -0.017049828 -0.016323818 -0.008659318
## Lag 5e+05 -0.029569144 0.007373270 -0.012037595
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.00000000
## Lag 1e+05 -0.0037002334 0.060768475 0.17981817
## Lag 2e+05 -0.0084642193 0.013189851 0.03394335
## Lag 3e+05 0.0101001085 0.005698145 0.02461184
## Lag 4e+05 0.0005335216 -0.048423829 0.02936621
## Lag 5e+05 0.0094867600 -0.042841997 0.02407725
## nodematch.race..wa.O
## Lag 0 1.0000000000
## Lag 1e+05 0.0209108263
## Lag 2e+05 0.0007050679
## Lag 3e+05 -0.0080459362
## Lag 4e+05 -0.0111292118
## Lag 5e+05 -0.0013889017
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -1.0490 0.2255 -0.5141
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -3.6554 0.8009 -1.2237
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.2544 -1.5960 0.6870
## nodematch.race..wa.O
## -0.1466
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.294184317 0.821572642 0.607206477
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.000256802 0.423202451 0.221079404
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.799150068 0.110495410 0.492074174
## nodematch.race..wa.O
## 0.883457834
## Joint P-value (lower = worse): 0.1508821 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -1.5147 -1.6801 -2.1700
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -0.7509 -0.1408 -0.1289
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## -1.1394 -0.6721 -1.0409
## nodematch.race..wa.O
## -1.5321
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.12984888 0.09293621 0.03000797
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.45271679 0.88805115 0.89744002
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.25453641 0.50152855 0.29791939
## nodematch.race..wa.O
## 0.12550757
## Joint P-value (lower = worse): 0.4563898 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -0.6518 -0.2847 -1.7650
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -0.1072 -0.7423 1.0809
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.6307 0.3387 -0.6409
## nodematch.race..wa.O
## -0.3134
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.51449815 0.77590896 0.07756342
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.91462249 0.45792108 0.27976069
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.52825787 0.73482665 0.52155549
## nodematch.race..wa.O
## 0.75400776
## Joint P-value (lower = worse): 0.5724516 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.4571 -0.5663 -0.6267
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.6473 1.2415 1.5685
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.5994 0.4101 0.7248
## nodematch.race..wa.O
## -0.3051
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.6476196 0.5711820 0.5308441
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.5174698 0.2144343 0.1167675
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.5489222 0.6817434 0.4685972
## nodematch.race..wa.O
## 0.7602913
## Joint P-value (lower = worse): 0.7888847 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -1.0537 0.2832 -1.3739
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -0.5158 -0.4967 0.9409
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.2611 -0.9178 -0.4263
## nodematch.race..wa.O
## -1.0203
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.2920161 0.7769981 0.1694727
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.6059743 0.6194177 0.3467701
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.7940015 0.3587356 0.6699013
## nodematch.race..wa.O
## 0.3075659
## Joint P-value (lower = worse): 0.6247722 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.1929 0.2821 0.6528
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -0.4709 0.2098 0.1931
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## -0.4073 0.6463 0.2205
## nodematch.race..wa.O
## 0.5227
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.8470064 0.7778700 0.5139096
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.6377199 0.8338511 0.8468983
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.6837844 0.5180803 0.8254459
## nodematch.race..wa.O
## 0.6011979
## Joint P-value (lower = worse): 0.9915551 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.6826 0.0792 -0.2890
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -1.1720 -0.4569 0.4955
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 1.1050 -0.5880 -1.1276
## nodematch.race..wa.O
## 1.1259
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.4948286 0.9368717 0.7726136
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.2412157 0.6477693 0.6202775
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.2691472 0.5565197 0.2595048
## nodematch.race..wa.O
## 0.2602236
## Joint P-value (lower = worse): 0.8623852 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## -0.2179 -1.0364 -0.5330
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## -0.5510 0.1624 0.8739
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.0282 0.2431 0.5000
## nodematch.race..wa.O
## 0.1317
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.pers.1 nodefactor.deg.pers.2
## 0.8275083 0.2999990 0.5940589
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## 0.5816317 0.8709774 0.3821600
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## 0.9775012 0.8079504 0.6170404
## nodematch.race..wa.O
## 0.8952017
## Joint P-value (lower = worse): 0.9503468 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Summary of model fit
Model 1
summary(est.m.testracemix.unbal[[1]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("race..wa", base = 3) + nodematch("race..wa",
## diff = TRUE) + degrange(from = 2) + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x560c19dc3808>
##
## Iterations: 150 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges 38.02 81345.40 100 1
## nodefactor.race..wa.B -47.33 81345.40 100 1
## nodefactor.race..wa.H -46.98 81345.40 100 1
## nodematch.race..wa.B 48.81 81345.40 100 1
## nodematch.race..wa.H 48.91 81345.40 100 1
## nodematch.race..wa.O -46.70 81345.40 100 1
## deg2+ -Inf 0.00 0 <1e-04 ***
## nodematch.role.class.I -Inf 0.00 0 <1e-04 ***
## nodematch.role.class.R -Inf 0.00 0 <1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg2+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238Model 2
summary(est.m.testracemix.unbal[[2]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.pers") + nodefactor("race..wa",
## base = 3) + nodematch("race..wa", diff = TRUE) + degrange(from = 2) +
## offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x560c3cd3a1a8>
##
## Iterations: 130 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges 4.039e+01 1.064e+04 100 0.9970
## nodefactor.deg.pers.1 -3.656e-01 6.279e-02 0 <1e-04 ***
## nodefactor.deg.pers.2 -1.012e-01 5.942e-02 0 0.0886 .
## nodefactor.race..wa.B -4.959e+01 1.064e+04 100 0.9963
## nodefactor.race..wa.H -4.924e+01 1.064e+04 100 0.9963
## nodematch.race..wa.B 5.107e+01 1.064e+04 100 0.9962
## nodematch.race..wa.H 5.117e+01 1.064e+04 100 0.9962
## nodematch.race..wa.O -4.896e+01 1.064e+04 100 0.9963
## deg2+ -Inf 0.000e+00 0 <1e-04 ***
## nodematch.role.class.I -Inf 0.000e+00 0 <1e-04 ***
## nodematch.role.class.R -Inf 0.000e+00 0 <1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg2+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238Model 3
summary(est.m.testracemix.unbal[[3]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("race..wa", base = 3) + nodefactor("region",
## base = 2) + nodematch("race..wa", diff = TRUE) + degrange(from = 2) +
## offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x560c5a0f0880>
##
## Iterations: 222 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges 5.572e+01 1.025e+05 100 0.99957
## nodefactor.race..wa.B -6.478e+01 1.025e+05 100 0.99950
## nodefactor.race..wa.H -6.438e+01 1.025e+05 100 0.99950
## nodefactor.region.EW -2.173e-01 6.920e-02 0 0.00169 **
## nodefactor.region.OW -3.893e-01 4.482e-02 0 < 1e-04 ***
## nodematch.race..wa.B 6.622e+01 1.025e+05 100 0.99948
## nodematch.race..wa.H 6.632e+01 1.025e+05 100 0.99948
## nodematch.race..wa.O -6.411e+01 1.025e+05 100 0.99950
## deg2+ -Inf 0.000e+00 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.000e+00 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.000e+00 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg2+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238Model 4
summary(est.m.testracemix.unbal[[4]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("deg.pers") + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + nodematch("race..wa",
## diff = TRUE) + degrange(from = 2) + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x560c7751fd30>
##
## Iterations: 147 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges 11.57002 NA NA NA
## nodefactor.deg.pers.1 -0.36531 0.06297 0 < 1e-04 ***
## nodefactor.deg.pers.2 -0.10251 0.05990 0 0.08698 .
## nodefactor.race..wa.B -20.50899 NA NA NA
## nodefactor.race..wa.H -20.10764 NA NA NA
## nodefactor.region.EW -0.21697 0.07003 0 0.00195 **
## nodefactor.region.OW -0.38970 0.04473 0 < 1e-04 ***
## nodematch.race..wa.B 21.94142 NA NA NA
## nodematch.race..wa.H 22.04793 NA NA NA
## nodematch.race..wa.O -19.83352 NA NA NA
## deg2+ -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## Warning: The following terms have infinite coefficient estimates:
## deg2+
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 153
## Crude Coefficient: 5.023881
## Mortality/Exit Rate: 5.302239e-05
## Adjusted Coefficient: 5.040238Network diagnostics
Model 1
(dx_main1 <- netdx(est.m.testracemix.unbal[[1]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.m.testracemix.unbal[[4]]$formation))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2240.500 2232.137 -0.004 30.520
## nodefactor.deg.pers.1 NA 567.430 NA 19.074
## nodefactor.deg.pers.2 NA 616.600 NA 20.842
## nodefactor.race..wa.B 207.997 210.423 0.012 11.201
## nodefactor.race..wa.H 534.978 534.352 -0.001 17.909
## nodefactor.region.EW NA 461.804 NA 14.972
## nodefactor.region.OW NA 1467.109 NA 28.580
## nodematch.race..wa.B 31.179 29.664 -0.049 5.028
## nodematch.race..wa.H 123.312 119.076 -0.034 9.588
## nodematch.race..wa.O 1638.890 1636.102 -0.002 27.873
## deg2+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 153.000 125.433 -0.180 119.964
## Pct Edges Diss 0.007 0.007 0.005 0.002plot(dx_main1, type="formation")
plot(dx_main1, type="duration")
plot(dx_main1, type="dissolution")
Model 2
(dx_main2 <- netdx(est.m.testracemix.unbal[[2]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.m.testracemix.unbal[[4]]$formation))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2240.500 2233.270 -0.003 30.943
## nodefactor.deg.pers.1 474.000 473.993 0.000 16.539
## nodefactor.deg.pers.2 605.000 601.848 -0.005 19.358
## nodefactor.race..wa.B 207.997 212.881 0.023 13.684
## nodefactor.race..wa.H 534.978 532.401 -0.005 18.153
## nodefactor.region.EW NA 461.188 NA 17.029
## nodefactor.region.OW NA 1465.580 NA 28.846
## nodematch.race..wa.B 31.179 29.009 -0.070 4.885
## nodematch.race..wa.H 123.312 120.546 -0.022 9.362
## nodematch.race..wa.O 1638.890 1637.544 -0.001 25.893
## deg2+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 153.000 125.912 -0.177 119.990
## Pct Edges Diss 0.007 0.007 0.000 0.002plot(dx_main2, type="formation")
plot(dx_main2, type="duration")
plot(dx_main2, type="dissolution")
Model 3
(dx_main3 <- netdx(est.m.testracemix.unbal[[3]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.m.testracemix.unbal[[4]]$formation))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2240.500 2230.253 -0.005 29.130
## nodefactor.deg.pers.1 NA 565.398 NA 16.701
## nodefactor.deg.pers.2 NA 615.811 NA 19.816
## nodefactor.race..wa.B 207.997 211.608 0.017 11.645
## nodefactor.race..wa.H 534.978 533.569 -0.003 18.127
## nodefactor.region.EW 445.561 441.524 -0.009 16.202
## nodefactor.region.OW 1278.131 1272.598 -0.004 33.041
## nodematch.race..wa.B 31.179 28.881 -0.074 4.633
## nodematch.race..wa.H 123.312 117.141 -0.050 8.387
## nodematch.race..wa.O 1638.890 1631.098 -0.005 26.456
## deg2+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 153.000 125.687 -0.179 120.352
## Pct Edges Diss 0.007 0.007 0.003 0.002plot(dx_main3, type="formation")
plot(dx_main3, type="duration")
plot(dx_main3, type="dissolution")
Model 4
(dx_main4 <- netdx(est.m.testracemix.unbal[[4]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.m.testracemix.unbal[[4]]$formation))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 2240.500 2234.023 -0.003 26.320
## nodefactor.deg.pers.1 474.000 472.558 -0.003 17.151
## nodefactor.deg.pers.2 605.000 602.140 -0.005 20.640
## nodefactor.race..wa.B 207.997 211.787 0.018 12.851
## nodefactor.race..wa.H 534.978 530.781 -0.008 14.943
## nodefactor.region.EW 445.561 443.445 -0.005 14.515
## nodefactor.region.OW 1278.131 1278.702 0.000 26.010
## nodematch.race..wa.B 31.179 29.126 -0.066 4.812
## nodematch.race..wa.H 123.312 116.716 -0.053 8.514
## nodematch.race..wa.O 1638.890 1637.296 -0.001 25.962
## deg2+ NA 0.000 NA 0.000
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 153.000 126.206 -0.175 120.361
## Pct Edges Diss 0.007 0.007 -0.002 0.002plot(dx_main4, type="formation")
plot(dx_main4, type="duration")
plot(dx_main4, type="dissolution")